class_docstr: Description
  -----------
  Compute a score for each node for sort-pooling. The score of each node
  is computed via the absolute difference of its first-order random walk
  result and its features.
  
  Arguments
  ---------
  sym_norm : bool, optional
      If true, use symmetric norm for adjacency.
      Default: :obj:`True`
  
  Parameters
  ----------
  graph : DGLGraph
      The graph to perform this operation.
  feat : torch.Tensor
      The node features
  e_feat : torch.Tensor, optional
      The edge features. Default: :obj:`None`
  
  Returns
  -------
  Tensor
      Score for each node.
code: def __init__(self, sym_norm: bool = True):
      super(NodeInfoScoreLayer, self).__init__()
      self.sym_norm = sym_norm
  
imports: import dgl
  import dgl.function
  import scipy.sparse
  import torch
  import torch.nn
  import torch.nn.functional
  from dgl import DGLGraph
  from dgl.nn import AvgPooling, GraphConv, MaxPooling
  from dgl.ops import edge_softmax
  from functions import edge_sparsemax
  from torch import Tensor
  from torch.nn import Parameter
  from utils import get_batch_id, topk
label_desc: Use image node_id 1 to create a new NodeInfoScoreLayer object from inherited base classes: nn with example: obj = NodeInfoScoreLayer(sym_norm)